Parallel programing with Cuda
git clone --recursive git@github.com:mrzhuzhe/riven.git
Submodules list:
- Eigen
- all application is compiler with cuda Arch 80 (RTX 3090) you can change it on CMakeLists
cd solver
# build
cmake -S src -B build
cmake --build build
# test_case
# 1. simple guassion elimination solver (as same as middle school)
test_simp
# 2. LU factor, PLU factor, PLU linear equation solver
test_lu
# 3. power method for eigen value, household process for simillar matrix, qr factor for eigen values
test_eigen
# 4. jacobian iteration, guassion_seidel iteration, multi grid method, conjugate gradient
test_iteration 32 1
# 5. conjugate gradient, biconjugate gradient, preconditioner(jacobian and Incomplete_Cholesky_factorization) conjugate gradient, GMRES, biconjugate gradient stablized
test_cg 32 1
both for CPU and CUDA
// x86 gemm
cd /gemm/
// nowaday the best result /MMult22_avx.c it's about 60GFlops (corresponding openBLAS is about 75GFLOPS )
// [TODO]use core-avx2 is much better than mavx
// [TODO] 8x6_avx is only about 40GFLOPS MMult22_avx3_8x6.c
// [TODO] inline volatile seems not work
// cuda gemm
cd /cuda_gemm/
// best result is /MMult_cuda_6_1.cu
cd /cuda_test/
// subfolders with corrosponding apps
/mm // shared and texture memory
/nn // a neural network like caffe
/warp // cuda concept about grid block warp and cooperate group
/pipline // cuda stream events
/pattern // application like convulition
# build
cmake -S src -B build
cmake --build build
- /RayTracing learn ray tracing in one week
- /openmp_test
- /llvm llvm totorial
- /cuda_fluid